ChartStory

ChartStory: Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives

Zhao, Jian (University of Waterloo)
Xu, Shenyu (Georgia Institute of Technology)
Chandrasegaran, R.S.K. (TU Delft Methodologie en Organisatie van Design; University of California)
Bryan, Christopher James (Arizona State University)
Du, Fan (Adobe Systems)
Mishra, Aditi (Arizona State University)
Qian, Xin (University of Maryland)
Li, Yiran (University of California)
Ma, Kwan Liu (University of California)

2022

Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline that characterizes charts by their properties and similarities to each other, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory's automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.

Annotations
Authoring systems
data comics
Data story generation
Data visualization
data-driven storytelling
Layout
narrative visualization
Pipelines
Tools
Visualization

http://resolver.tudelft.nl/uuid:af369f3a-96ad-49f9-9b53-04ca65bdadf0

https://doi.org/10.1109/TVCG.2021.3114211

2023-07-01

1077-2626

IEEE Transactions on Visualization and Computer Graphics, 29 (2), 1384-1399

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Jian Zhao, Shenyu Xu, R.S.K. Chandrasegaran, Christopher James Bryan, Fan Du, Aditi Mishra, Xin Qian, Yiran Li, Kwan Liu Ma